Overview

Dataset statistics

Number of variables14
Number of observations455
Missing cells0
Missing cells (%)0.0%
Duplicate rows55
Duplicate rows (%)12.1%
Total size in memory341.3 KiB
Average record size in memory768.2 B

Variable types

Categorical7
Numeric7

Alerts

Dataset has 55 (12.1%) duplicate rowsDuplicates

Reproduction

Analysis started2024-06-07 12:54:29.824733
Analysis finished2024-06-07 12:54:33.178989
Duration3.35 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

P2
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size29.2 KiB
Femenino
235 
Masculino
220 

Length

Max length9
Median length8
Mean length8.4835165
Min length8

Characters and Unicode

Total characters3860
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMasculino
2nd rowMasculino
3rd rowMasculino
4th rowMasculino
5th rowMasculino

Common Values

ValueCountFrequency (%)
Femenino 235
51.6%
Masculino 220
48.4%

Length

2024-06-07T09:54:33.225732image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-07T09:54:33.273449image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
femenino 235
51.6%
masculino 220
48.4%

Most occurring characters

ValueCountFrequency (%)
n 690
17.9%
e 470
12.2%
o 455
11.8%
i 455
11.8%
m 235
 
6.1%
F 235
 
6.1%
M 220
 
5.7%
a 220
 
5.7%
s 220
 
5.7%
c 220
 
5.7%
Other values (2) 440
11.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3860
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 690
17.9%
e 470
12.2%
o 455
11.8%
i 455
11.8%
m 235
 
6.1%
F 235
 
6.1%
M 220
 
5.7%
a 220
 
5.7%
s 220
 
5.7%
c 220
 
5.7%
Other values (2) 440
11.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3860
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 690
17.9%
e 470
12.2%
o 455
11.8%
i 455
11.8%
m 235
 
6.1%
F 235
 
6.1%
M 220
 
5.7%
a 220
 
5.7%
s 220
 
5.7%
c 220
 
5.7%
Other values (2) 440
11.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3860
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 690
17.9%
e 470
12.2%
o 455
11.8%
i 455
11.8%
m 235
 
6.1%
F 235
 
6.1%
M 220
 
5.7%
a 220
 
5.7%
s 220
 
5.7%
c 220
 
5.7%
Other values (2) 440
11.4%

P3a
Categorical

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size38.2 KiB
Más de 50
277 
30-49
147 
18-29
31 

Length

Max length9
Median length9
Mean length7.4351648
Min length5

Characters and Unicode

Total characters3383
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row18-29
2nd row18-29
3rd row30-49
4th row30-49
5th row30-49

Common Values

ValueCountFrequency (%)
Más de 50 277
60.9%
30-49 147
32.3%
18-29 31
 
6.8%

Length

2024-06-07T09:54:33.333892image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-07T09:54:33.386675image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
más 277
27.5%
de 277
27.5%
50 277
27.5%
30-49 147
14.6%
18-29 31
 
3.1%

Most occurring characters

ValueCountFrequency (%)
554
16.4%
0 424
12.5%
á 277
8.2%
s 277
8.2%
d 277
8.2%
e 277
8.2%
M 277
8.2%
5 277
8.2%
- 178
 
5.3%
9 178
 
5.3%
Other values (5) 387
11.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3383
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
554
16.4%
0 424
12.5%
á 277
8.2%
s 277
8.2%
d 277
8.2%
e 277
8.2%
M 277
8.2%
5 277
8.2%
- 178
 
5.3%
9 178
 
5.3%
Other values (5) 387
11.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3383
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
554
16.4%
0 424
12.5%
á 277
8.2%
s 277
8.2%
d 277
8.2%
e 277
8.2%
M 277
8.2%
5 277
8.2%
- 178
 
5.3%
9 178
 
5.3%
Other values (5) 387
11.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3383
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
554
16.4%
0 424
12.5%
á 277
8.2%
s 277
8.2%
d 277
8.2%
e 277
8.2%
M 277
8.2%
5 277
8.2%
- 178
 
5.3%
9 178
 
5.3%
Other values (5) 387
11.4%

P4
Categorical

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size49.8 KiB
Nivel Universitario o Terciario, ya sea completo o incompleto
275 
Nivel Secundario, ya sea completo o incompleto
131 
Nivel Primario, ya sea completo o incompleto
49 

Length

Max length61
Median length61
Mean length54.850549
Min length44

Characters and Unicode

Total characters24957
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNivel Primario, ya sea completo o incompleto
2nd rowNivel Primario, ya sea completo o incompleto
3rd rowNivel Primario, ya sea completo o incompleto
4th rowNivel Primario, ya sea completo o incompleto
5th rowNivel Primario, ya sea completo o incompleto

Common Values

ValueCountFrequency (%)
Nivel Universitario o Terciario, ya sea completo o incompleto 275
60.4%
Nivel Secundario, ya sea completo o incompleto 131
28.8%
Nivel Primario, ya sea completo o incompleto 49
 
10.8%

Length

2024-06-07T09:54:33.446138image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-07T09:54:33.496380image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
o 730
19.5%
nivel 455
12.2%
incompleto 455
12.2%
ya 455
12.2%
sea 455
12.2%
completo 455
12.2%
universitario 275
 
7.4%
terciario 275
 
7.4%
secundario 131
 
3.5%
primario 49
 
1.3%

Most occurring characters

ValueCountFrequency (%)
3280
13.1%
o 3280
13.1%
i 2514
10.1%
e 2501
10.0%
a 1640
 
6.6%
l 1365
 
5.5%
r 1329
 
5.3%
c 1316
 
5.3%
t 1185
 
4.7%
m 959
 
3.8%
Other values (13) 5588
22.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24957
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3280
13.1%
o 3280
13.1%
i 2514
10.1%
e 2501
10.0%
a 1640
 
6.6%
l 1365
 
5.5%
r 1329
 
5.3%
c 1316
 
5.3%
t 1185
 
4.7%
m 959
 
3.8%
Other values (13) 5588
22.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24957
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3280
13.1%
o 3280
13.1%
i 2514
10.1%
e 2501
10.0%
a 1640
 
6.6%
l 1365
 
5.5%
r 1329
 
5.3%
c 1316
 
5.3%
t 1185
 
4.7%
m 959
 
3.8%
Other values (13) 5588
22.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24957
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3280
13.1%
o 3280
13.1%
i 2514
10.1%
e 2501
10.0%
a 1640
 
6.6%
l 1365
 
5.5%
r 1329
 
5.3%
c 1316
 
5.3%
t 1185
 
4.7%
m 959
 
3.8%
Other values (13) 5588
22.4%

P4.1
Categorical

Distinct7
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size57.3 KiB
Patricia Bullrich
186 
Sergio Massa y Agustín Rossi del Frente de Todos/Unión por la Patria
136 
Javier Milei y Victoria Villarruel de La Libertad Avanza
84 
En blanco/ No votaría
20 
No sabe / No contesta
 
18
Other values (2)
 
11

Length

Max length85
Median length68
Mean length40.32967
Min length14

Characters and Unicode

Total characters18350
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSergio Massa y Agustín Rossi del Frente de Todos/Unión por la Patria
2nd rowJavier Milei y Victoria Villarruel de La Libertad Avanza
3rd rowEn blanco/ No votaría
4th rowSergio Massa y Agustín Rossi del Frente de Todos/Unión por la Patria
5th rowPatricia Bullrich

Common Values

ValueCountFrequency (%)
Patricia Bullrich 186
40.9%
Sergio Massa y Agustín Rossi del Frente de Todos/Unión por la Patria 136
29.9%
Javier Milei y Victoria Villarruel de La Libertad Avanza 84
18.5%
En blanco/ No votaría 20
 
4.4%
No sabe / No contesta 18
 
4.0%
Myriam Bregman 7
 
1.5%
Juan Schiaretti y Florencio Randazzo del Peronismo Federal / Hacemos por Nuestro País 4
 
0.9%

Length

2024-06-07T09:54:33.565665image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-07T09:54:33.629556image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
y 224
 
7.5%
de 220
 
7.3%
la 220
 
7.3%
patricia 186
 
6.2%
bullrich 186
 
6.2%
por 140
 
4.7%
del 140
 
4.7%
agustín 136
 
4.5%
sergio 136
 
4.5%
massa 136
 
4.5%
Other values (28) 1272
42.5%

Most occurring characters

ValueCountFrequency (%)
2541
13.8%
a 1778
 
9.7%
i 1713
 
9.3%
r 1394
 
7.6%
e 1175
 
6.4%
l 1012
 
5.5%
o 910
 
5.0%
s 868
 
4.7%
t 830
 
4.5%
n 709
 
3.9%
Other values (29) 5420
29.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18350
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2541
13.8%
a 1778
 
9.7%
i 1713
 
9.3%
r 1394
 
7.6%
e 1175
 
6.4%
l 1012
 
5.5%
o 910
 
5.0%
s 868
 
4.7%
t 830
 
4.5%
n 709
 
3.9%
Other values (29) 5420
29.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18350
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2541
13.8%
a 1778
 
9.7%
i 1713
 
9.3%
r 1394
 
7.6%
e 1175
 
6.4%
l 1012
 
5.5%
o 910
 
5.0%
s 868
 
4.7%
t 830
 
4.5%
n 709
 
3.9%
Other values (29) 5420
29.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18350
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2541
13.8%
a 1778
 
9.7%
i 1713
 
9.3%
r 1394
 
7.6%
e 1175
 
6.4%
l 1012
 
5.5%
o 910
 
5.0%
s 868
 
4.7%
t 830
 
4.5%
n 709
 
3.9%
Other values (29) 5420
29.5%

P5
Categorical

Distinct6
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size45.6 KiB
Jorge Macri de Juntos
205 
Leandro Santoro de Unión por la Patria
111 
Ramiro Marra de la Libertad avanza
60 
No sabe/ no contesta
41 
En blanco/ No votaría
29 

Length

Max length38
Median length21
Mean length26.751648
Min length20

Characters and Unicode

Total characters12172
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLeandro Santoro de Unión por la Patria
2nd rowNo sabe/ no contesta
3rd rowEn blanco/ No votaría
4th rowLeandro Santoro de Unión por la Patria
5th rowJorge Macri de Juntos

Common Values

ValueCountFrequency (%)
Jorge Macri de Juntos 205
45.1%
Leandro Santoro de Unión por la Patria 111
24.4%
Ramiro Marra de la Libertad avanza 60
 
13.2%
No sabe/ no contesta 41
 
9.0%
En blanco/ No votaría 29
 
6.4%
Vanina Biasi del FIT 9
 
2.0%

Length

2024-06-07T09:54:33.716837image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-07T09:54:33.775038image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
de 376
16.5%
jorge 205
 
9.0%
macri 205
 
9.0%
juntos 205
 
9.0%
la 171
 
7.5%
leandro 111
 
4.9%
unión 111
 
4.9%
santoro 111
 
4.9%
por 111
 
4.9%
patria 111
 
4.9%
Other values (14) 556
24.5%

Most occurring characters

ValueCountFrequency (%)
1818
14.9%
a 1436
11.8%
o 1124
 
9.2%
r 1123
 
9.2%
n 867
 
7.1%
e 843
 
6.9%
t 598
 
4.9%
i 574
 
4.7%
d 556
 
4.6%
J 410
 
3.4%
Other values (26) 2823
23.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12172
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1818
14.9%
a 1436
11.8%
o 1124
 
9.2%
r 1123
 
9.2%
n 867
 
7.1%
e 843
 
6.9%
t 598
 
4.9%
i 574
 
4.7%
d 556
 
4.6%
J 410
 
3.4%
Other values (26) 2823
23.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12172
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1818
14.9%
a 1436
11.8%
o 1124
 
9.2%
r 1123
 
9.2%
n 867
 
7.1%
e 843
 
6.9%
t 598
 
4.9%
i 574
 
4.7%
d 556
 
4.6%
J 410
 
3.4%
Other values (26) 2823
23.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12172
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1818
14.9%
a 1436
11.8%
o 1124
 
9.2%
r 1123
 
9.2%
n 867
 
7.1%
e 843
 
6.9%
t 598
 
4.9%
i 574
 
4.7%
d 556
 
4.6%
J 410
 
3.4%
Other values (26) 2823
23.2%

P6
Categorical

Distinct10
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size60.6 KiB
Patricia Bullrich por Juntos por el Cambio
139 
Sergio Massa y Agustín Rossi del Frente de Todos/Unión por la Patria
98 
Javier Milei y Victoria Villarruel de La Libertad Avanza
67 
Horacio R Larreta
53 
Juan Grabois y Paula Abal Medina del Frente de Todos/Unión por la Patria
38 
Other values (5)
60 

Length

Max length85
Median length72
Mean length46.874725
Min length4

Characters and Unicode

Total characters21328
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSergio Massa y Agustín Rossi del Frente de Todos/Unión por la Patria
2nd rowEn blanco/ No votaría
3rd rowOtro
4th rowJavier Milei y Victoria Villarruel de La Libertad Avanza
5th rowPatricia Bullrich por Juntos por el Cambio

Common Values

ValueCountFrequency (%)
Patricia Bullrich por Juntos por el Cambio 139
30.5%
Sergio Massa y Agustín Rossi del Frente de Todos/Unión por la Patria 98
21.5%
Javier Milei y Victoria Villarruel de La Libertad Avanza 67
14.7%
Horacio R Larreta 53
 
11.6%
Juan Grabois y Paula Abal Medina del Frente de Todos/Unión por la Patria 38
 
8.4%
En blanco/ No votaría 25
 
5.5%
No sabe / No contesta 14
 
3.1%
Otro 10
 
2.2%
Myriam Bregman o Solano por el FIT 7
 
1.5%
Juan Schiaretti y Florencio Randazzo del Peronismo Federal / Hacemos por Nuestro País 4
 
0.9%

Length

2024-06-07T09:54:33.859179image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-07T09:54:33.933955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
por 425
 
11.5%
y 207
 
5.6%
de 203
 
5.5%
la 203
 
5.5%
el 146
 
4.0%
del 140
 
3.8%
patricia 139
 
3.8%
bullrich 139
 
3.8%
juntos 139
 
3.8%
cambio 139
 
3.8%
Other values (42) 1806
49.0%

Most occurring characters

ValueCountFrequency (%)
3231
15.1%
a 2029
 
9.5%
r 1674
 
7.8%
i 1645
 
7.7%
o 1558
 
7.3%
e 1281
 
6.0%
l 1084
 
5.1%
t 910
 
4.3%
n 882
 
4.1%
s 847
 
4.0%
Other values (33) 6187
29.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21328
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3231
15.1%
a 2029
 
9.5%
r 1674
 
7.8%
i 1645
 
7.7%
o 1558
 
7.3%
e 1281
 
6.0%
l 1084
 
5.1%
t 910
 
4.3%
n 882
 
4.1%
s 847
 
4.0%
Other values (33) 6187
29.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21328
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3231
15.1%
a 2029
 
9.5%
r 1674
 
7.8%
i 1645
 
7.7%
o 1558
 
7.3%
e 1281
 
6.0%
l 1084
 
5.1%
t 910
 
4.3%
n 882
 
4.1%
s 847
 
4.0%
Other values (33) 6187
29.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21328
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3231
15.1%
a 2029
 
9.5%
r 1674
 
7.8%
i 1645
 
7.7%
o 1558
 
7.3%
e 1281
 
6.0%
l 1084
 
5.1%
t 910
 
4.3%
n 882
 
4.1%
s 847
 
4.0%
Other values (33) 6187
29.0%

P7
Categorical

Distinct8
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size36.4 KiB
Jorge Macri de JXC
160 
Leandro Santoro de UPX
98 
M Lousteau de JXC
77 
Ramiro Marra de LA
39 
En blanco/ No votaría
34 
Other values (3)
47 

Length

Max length33
Median length28
Mean length19.063736
Min length5

Characters and Unicode

Total characters8674
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVanina Biasi o Adaro del FIT
2nd rowEn blanco/ No votaría
3rd rowVanina Biasi o Adaro del FIT
4th rowM Lousteau de JXC
5th rowNo sabe/ No tiene opinión formada

Common Values

ValueCountFrequency (%)
Jorge Macri de JXC 160
35.2%
Leandro Santoro de UPX 98
21.5%
M Lousteau de JXC 77
16.9%
Ramiro Marra de LA 39
 
8.6%
En blanco/ No votaría 34
 
7.5%
Otros 21
 
4.6%
No sabe/ No tiene opinión formada 16
 
3.5%
Vanina Biasi o Adaro del FIT 10
 
2.2%

Length

2024-06-07T09:54:34.027063image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-07T09:54:34.087359image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
de 374
20.7%
jxc 237
13.1%
jorge 160
8.8%
macri 160
8.8%
leandro 98
 
5.4%
santoro 98
 
5.4%
upx 98
 
5.4%
m 77
 
4.3%
lousteau 77
 
4.3%
no 66
 
3.6%
Other values (17) 364
20.1%

Most occurring characters

ValueCountFrequency (%)
1354
15.6%
o 777
 
9.0%
e 767
 
8.8%
a 740
 
8.5%
r 714
 
8.2%
d 508
 
5.9%
J 397
 
4.6%
X 335
 
3.9%
n 332
 
3.8%
i 277
 
3.2%
Other values (30) 2473
28.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8674
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1354
15.6%
o 777
 
9.0%
e 767
 
8.8%
a 740
 
8.5%
r 714
 
8.2%
d 508
 
5.9%
J 397
 
4.6%
X 335
 
3.9%
n 332
 
3.8%
i 277
 
3.2%
Other values (30) 2473
28.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8674
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1354
15.6%
o 777
 
9.0%
e 767
 
8.8%
a 740
 
8.5%
r 714
 
8.2%
d 508
 
5.9%
J 397
 
4.6%
X 335
 
3.9%
n 332
 
3.8%
i 277
 
3.2%
Other values (30) 2473
28.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8674
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1354
15.6%
o 777
 
9.0%
e 767
 
8.8%
a 740
 
8.5%
r 714
 
8.2%
d 508
 
5.9%
J 397
 
4.6%
X 335
 
3.9%
n 332
 
3.8%
i 277
 
3.2%
Other values (30) 2473
28.5%

Ponde
Real number (ℝ)

Distinct14
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.95362637
Minimum0.6
Maximum2.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-06-07T09:54:34.153558image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile0.7
Q10.8
median0.9
Q31
95-th percentile1.5
Maximum2.1
Range1.5
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.23405785
Coefficient of variation (CV)0.24543978
Kurtosis4.0355414
Mean0.95362637
Median Absolute Deviation (MAD)0.1
Skewness1.7610251
Sum433.9
Variance0.054783076
MonotonicityNot monotonic
2024-06-07T09:54:34.216520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.8 115
25.3%
0.9 104
22.9%
1 102
22.4%
1.1 39
 
8.6%
0.7 27
 
5.9%
0.6 19
 
4.2%
1.2 12
 
2.6%
1.5 9
 
2.0%
1.7 8
 
1.8%
1.6 7
 
1.5%
Other values (4) 13
 
2.9%
ValueCountFrequency (%)
0.6 19
 
4.2%
0.7 27
 
5.9%
0.8 115
25.3%
0.9 104
22.9%
1 102
22.4%
1.1 39
 
8.6%
1.2 12
 
2.6%
1.3 5
 
1.1%
1.4 2
 
0.4%
1.5 9
 
2.0%
ValueCountFrequency (%)
2.1 1
 
0.2%
1.8 5
 
1.1%
1.7 8
 
1.8%
1.6 7
 
1.5%
1.5 9
 
2.0%
1.4 2
 
0.4%
1.3 5
 
1.1%
1.2 12
 
2.6%
1.1 39
 
8.6%
1 102
22.4%

Ponde2
Real number (ℝ)

Distinct46
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9354945
Minimum3.2
Maximum8.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-06-07T09:54:34.298017image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum3.2
5-th percentile3.2
Q13.3
median3.73
Q34.1
95-th percentile6.36
Maximum8.43
Range5.23
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.90352255
Coefficient of variation (CV)0.22958298
Kurtosis5.1375014
Mean3.9354945
Median Absolute Deviation (MAD)0.43
Skewness2.2689041
Sum1790.65
Variance0.816353
MonotonicityNot monotonic
2024-06-07T09:54:34.386725image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
3.2 74
16.3%
3.3 64
14.1%
3.7 35
 
7.7%
3.83 30
 
6.6%
3.73 21
 
4.6%
3.8 21
 
4.6%
3.6 20
 
4.4%
4.23 19
 
4.2%
3.4 17
 
3.7%
4.1 15
 
3.3%
Other values (36) 139
30.5%
ValueCountFrequency (%)
3.2 74
16.3%
3.3 64
14.1%
3.4 17
 
3.7%
3.5 5
 
1.1%
3.6 20
 
4.4%
3.7 35
7.7%
3.71 4
 
0.9%
3.73 21
 
4.6%
3.8 21
 
4.6%
3.81 12
 
2.6%
ValueCountFrequency (%)
8.43 1
 
0.2%
7.16 6
1.3%
7.14 1
 
0.2%
6.86 2
 
0.4%
6.84 1
 
0.2%
6.76 3
0.7%
6.74 3
0.7%
6.63 4
0.9%
6.53 1
 
0.2%
6.43 1
 
0.2%

Sociodemografico
Real number (ℝ)

Distinct7
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.98857143
Minimum0.8
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-06-07T09:54:34.458515image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile0.8
Q10.8
median1
Q31
95-th percentile1.8
Maximum2
Range1.2
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.27009776
Coefficient of variation (CV)0.27322028
Kurtosis6.3802488
Mean0.98857143
Median Absolute Deviation (MAD)0.1
Skewness2.538182
Sum449.8
Variance0.072952801
MonotonicityNot monotonic
2024-06-07T09:54:34.525852image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.8 178
39.1%
1 143
31.4%
1.1 65
 
14.3%
0.9 38
 
8.4%
1.8 15
 
3.3%
2 13
 
2.9%
1.9 3
 
0.7%
ValueCountFrequency (%)
0.8 178
39.1%
0.9 38
 
8.4%
1 143
31.4%
1.1 65
 
14.3%
1.8 15
 
3.3%
1.9 3
 
0.7%
2 13
 
2.9%
ValueCountFrequency (%)
2 13
 
2.9%
1.9 3
 
0.7%
1.8 15
 
3.3%
1.1 65
 
14.3%
1 143
31.4%
0.9 38
 
8.4%
0.8 178
39.1%

PondeJdg
Real number (ℝ)

Distinct13
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.98857143
Minimum0.6
Maximum1.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-06-07T09:54:34.588985image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile0.7
Q10.8
median1
Q31.1
95-th percentile1.6
Maximum1.8
Range1.2
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.22712807
Coefficient of variation (CV)0.22975383
Kurtosis2.5812932
Mean0.98857143
Median Absolute Deviation (MAD)0.1
Skewness1.3337527
Sum449.8
Variance0.051587162
MonotonicityNot monotonic
2024-06-07T09:54:34.657898image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 103
22.6%
0.8 91
20.0%
0.9 88
19.3%
1.1 73
16.0%
1.2 25
 
5.5%
0.7 19
 
4.2%
0.6 16
 
3.5%
1.3 12
 
2.6%
1.7 10
 
2.2%
1.6 10
 
2.2%
Other values (3) 8
 
1.8%
ValueCountFrequency (%)
0.6 16
 
3.5%
0.7 19
 
4.2%
0.8 91
20.0%
0.9 88
19.3%
1 103
22.6%
1.1 73
16.0%
1.2 25
 
5.5%
1.3 12
 
2.6%
1.4 2
 
0.4%
1.5 2
 
0.4%
ValueCountFrequency (%)
1.8 4
 
0.9%
1.7 10
 
2.2%
1.6 10
 
2.2%
1.5 2
 
0.4%
1.4 2
 
0.4%
1.3 12
 
2.6%
1.2 25
 
5.5%
1.1 73
16.0%
1 103
22.6%
0.9 88
19.3%

SoloJGB
Real number (ℝ)

Distinct6
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1094945
Minimum0.78
Maximum1.72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-06-07T09:54:34.718216image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.78
5-th percentile0.87
Q10.87
median1
Q31.32
95-th percentile1.72
Maximum1.72
Range0.94
Interquartile range (IQR)0.45

Descriptive statistics

Standard deviation0.30375181
Coefficient of variation (CV)0.27377496
Kurtosis-0.023425415
Mean1.1094945
Median Absolute Deviation (MAD)0.13
Skewness1.2011105
Sum504.82
Variance0.092265162
MonotonicityNot monotonic
2024-06-07T09:54:34.774380image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0.87 160
35.2%
1.05 98
21.5%
1.72 77
16.9%
1 71
15.6%
1.32 39
 
8.6%
0.78 10
 
2.2%
ValueCountFrequency (%)
0.78 10
 
2.2%
0.87 160
35.2%
1 71
15.6%
1.05 98
21.5%
1.32 39
 
8.6%
1.72 77
16.9%
ValueCountFrequency (%)
1.72 77
16.9%
1.32 39
 
8.6%
1.05 98
21.5%
1 71
15.6%
0.87 160
35.2%
0.78 10
 
2.2%

EdadmasJGB
Real number (ℝ)

Distinct11
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0685714
Minimum0.7
Maximum2.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-06-07T09:54:34.830357image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile0.8
Q10.8
median1
Q31.2
95-th percentile2.2
Maximum2.7
Range2
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.38324079
Coefficient of variation (CV)0.3586478
Kurtosis5.7300856
Mean1.0685714
Median Absolute Deviation (MAD)0.2
Skewness2.4227346
Sum486.2
Variance0.14687351
MonotonicityNot monotonic
2024-06-07T09:54:34.895722image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0.8 148
32.5%
1 79
17.4%
1.2 76
16.7%
0.9 64
14.1%
1.1 33
 
7.3%
1.4 21
 
4.6%
2.3 14
 
3.1%
2.2 9
 
2.0%
2.5 7
 
1.5%
0.7 3
 
0.7%
ValueCountFrequency (%)
0.7 3
 
0.7%
0.8 148
32.5%
0.9 64
14.1%
1 79
17.4%
1.1 33
 
7.3%
1.2 76
16.7%
1.4 21
 
4.6%
2.2 9
 
2.0%
2.3 14
 
3.1%
2.5 7
 
1.5%
ValueCountFrequency (%)
2.7 1
 
0.2%
2.5 7
 
1.5%
2.3 14
 
3.1%
2.2 9
 
2.0%
1.4 21
 
4.6%
1.2 76
16.7%
1.1 33
 
7.3%
1 79
17.4%
0.9 64
14.1%
0.8 148
32.5%

EdadJGB
Real number (ℝ)

Distinct6
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1094945
Minimum0.78
Maximum1.72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-06-07T09:54:34.961179image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.78
5-th percentile0.87
Q10.87
median1
Q31.32
95-th percentile1.72
Maximum1.72
Range0.94
Interquartile range (IQR)0.45

Descriptive statistics

Standard deviation0.30375181
Coefficient of variation (CV)0.27377496
Kurtosis-0.023425415
Mean1.1094945
Median Absolute Deviation (MAD)0.13
Skewness1.2011105
Sum504.82
Variance0.092265162
MonotonicityNot monotonic
2024-06-07T09:54:35.017620image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0.87 160
35.2%
1.05 98
21.5%
1.72 77
16.9%
1 71
15.6%
1.32 39
 
8.6%
0.78 10
 
2.2%
ValueCountFrequency (%)
0.78 10
 
2.2%
0.87 160
35.2%
1 71
15.6%
1.05 98
21.5%
1.32 39
 
8.6%
1.72 77
16.9%
ValueCountFrequency (%)
1.72 77
16.9%
1.32 39
 
8.6%
1.05 98
21.5%
1 71
15.6%
0.87 160
35.2%
0.78 10
 
2.2%

Interactions

2024-06-07T09:54:32.429575image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:29.998433image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:30.406209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:30.806363image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:31.206666image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:31.601664image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:31.992707image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:32.489196image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:30.061311image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:30.464904image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:30.865723image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:31.266787image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:31.658042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:32.059960image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:32.544146image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:30.118152image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:30.519777image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:30.922294image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:31.319552image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:31.710255image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:32.119937image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:32.601336image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:30.175519image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:30.583262image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:30.980626image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:31.377528image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:31.768706image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:32.184642image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:32.657003image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:30.235538image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:30.643686image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:31.037925image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:31.433682image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:31.825886image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:32.247350image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:32.710776image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:30.286417image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:30.693682image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:31.090856image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:31.487515image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:31.878217image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:32.305166image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:32.772753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:30.351951image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:30.756385image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:31.152859image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:31.548927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:31.941300image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-07T09:54:32.371071image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-06-07T09:54:32.854344image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-07T09:54:33.132745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

P2P3aP4P4.1P5P6P7PondePonde2SociodemograficoPondeJdgSoloJGBEdadmasJGBEdadJGB
0Masculino18-29Nivel Primario, ya sea completo o incompletoSergio Massa y Agustín Rossi del Frente de Todos/Unión por la PatriaLeandro Santoro de Unión por la PatriaSergio Massa y Agustín Rossi del Frente de Todos/Unión por la PatriaVanina Biasi o Adaro del FIT1.76.842.01.70.782.20.78
1Masculino18-29Nivel Primario, ya sea completo o incompletoJavier Milei y Victoria Villarruel de La Libertad AvanzaNo sabe/ no contestaEn blanco/ No votaríaEn blanco/ No votaría1.87.142.01.71.002.31.00
2Masculino30-49Nivel Primario, ya sea completo o incompletoEn blanco/ No votaríaEn blanco/ No votaríaOtroVanina Biasi o Adaro del FIT0.84.411.10.80.780.90.78
3Masculino30-49Nivel Primario, ya sea completo o incompletoSergio Massa y Agustín Rossi del Frente de Todos/Unión por la PatriaLeandro Santoro de Unión por la PatriaJavier Milei y Victoria Villarruel de La Libertad AvanzaM Lousteau de JXC1.14.311.11.31.721.41.72
4Masculino30-49Nivel Primario, ya sea completo o incompletoPatricia BullrichJorge Macri de JuntosPatricia Bullrich por Juntos por el CambioNo sabe/ No tiene opinión formada1.04.211.11.11.001.01.00
5Masculino30-49Nivel Primario, ya sea completo o incompletoNo sabe / No contestaNo sabe/ no contestaSergio Massa y Agustín Rossi del Frente de Todos/Unión por la PatriaJorge Macri de JXC0.84.411.10.80.871.00.87
6Masculino30-49Nivel Primario, ya sea completo o incompletoJavier Milei y Victoria Villarruel de La Libertad AvanzaRamiro Marra de la Libertad avanzaJavier Milei y Victoria Villarruel de La Libertad AvanzaRamiro Marra de LA1.14.611.11.21.321.21.32
7Masculino30-49Nivel Primario, ya sea completo o incompletoSergio Massa y Agustín Rossi del Frente de Todos/Unión por la PatriaJorge Macri de JuntosSergio Massa y Agustín Rossi del Frente de Todos/Unión por la PatriaJorge Macri de JXC1.14.311.11.00.871.00.87
8Masculino30-49Nivel Primario, ya sea completo o incompletoJavier Milei y Victoria Villarruel de La Libertad AvanzaEn blanco/ No votaríaJavier Milei y Victoria Villarruel de La Libertad AvanzaEn blanco/ No votaría0.94.611.10.81.001.01.00
9Masculino30-49Nivel Primario, ya sea completo o incompletoSergio Massa y Agustín Rossi del Frente de Todos/Unión por la PatriaNo sabe/ no contestaSergio Massa y Agustín Rossi del Frente de Todos/Unión por la PatriaNo sabe/ No tiene opinión formada1.14.311.11.11.001.01.00
P2P3aP4P4.1P5P6P7PondePonde2SociodemograficoPondeJdgSoloJGBEdadmasJGBEdadJGB
445FemeninoMás de 50Nivel Universitario o Terciario, ya sea completo o incompletoPatricia BullrichJorge Macri de JuntosPatricia Bullrich por Juntos por el CambioM Lousteau de JXC0.83.20.81.11.721.21.72
446FemeninoMás de 50Nivel Universitario o Terciario, ya sea completo o incompletoPatricia BullrichRamiro Marra de la Libertad avanzaPatricia Bullrich por Juntos por el CambioJorge Macri de JXC0.83.20.80.80.870.80.87
447FemeninoMás de 50Nivel Universitario o Terciario, ya sea completo o incompletoPatricia BullrichJorge Macri de JuntosPatricia Bullrich por Juntos por el CambioJorge Macri de JXC0.83.20.80.80.870.80.87
448FemeninoMás de 50Nivel Universitario o Terciario, ya sea completo o incompletoPatricia BullrichJorge Macri de JuntosPatricia Bullrich por Juntos por el CambioJorge Macri de JXC0.83.20.80.80.870.80.87
449FemeninoMás de 50Nivel Universitario o Terciario, ya sea completo o incompletoPatricia BullrichJorge Macri de JuntosPatricia Bullrich por Juntos por el CambioJorge Macri de JXC0.83.20.80.80.870.80.87
450FemeninoMás de 50Nivel Universitario o Terciario, ya sea completo o incompletoJavier Milei y Victoria Villarruel de La Libertad AvanzaRamiro Marra de la Libertad avanzaJavier Milei y Victoria Villarruel de La Libertad AvanzaJorge Macri de JXC0.93.60.80.80.870.80.87
451FemeninoMás de 50Nivel Universitario o Terciario, ya sea completo o incompletoEn blanco/ No votaríaEn blanco/ No votaríaHoracio R LarretaEn blanco/ No votaría0.93.40.80.91.000.81.00
452FemeninoMás de 50Nivel Universitario o Terciario, ya sea completo o incompletoPatricia BullrichJorge Macri de JuntosHoracio R LarretaJorge Macri de JXC0.83.20.80.80.870.80.87
453FemeninoMás de 50Nivel Universitario o Terciario, ya sea completo o incompletoPatricia BullrichJorge Macri de JuntosEn blanco/ No votaríaJorge Macri de JXC0.83.20.80.80.870.80.87
454FemeninoMás de 50Nivel Universitario o Terciario, ya sea completo o incompletoSergio Massa y Agustín Rossi del Frente de Todos/Unión por la PatriaLeandro Santoro de Unión por la PatriaSergio Massa y Agustín Rossi del Frente de Todos/Unión por la PatriaLeandro Santoro de UPX0.93.30.80.91.050.91.05

Duplicate rows

Most frequently occurring

P2P3aP4P4.1P5P6P7PondePonde2SociodemograficoPondeJdgSoloJGBEdadmasJGBEdadJGB# duplicates
19FemeninoMás de 50Nivel Universitario o Terciario, ya sea completo o incompletoPatricia BullrichJorge Macri de JuntosPatricia Bullrich por Juntos por el CambioJorge Macri de JXC0.83.200.80.80.870.80.8740
24FemeninoMás de 50Nivel Universitario o Terciario, ya sea completo o incompletoSergio Massa y Agustín Rossi del Frente de Todos/Unión por la PatriaLeandro Santoro de Unión por la PatriaSergio Massa y Agustín Rossi del Frente de Todos/Unión por la PatriaLeandro Santoro de UPX0.93.300.80.91.050.91.0515
50MasculinoMás de 50Nivel Universitario o Terciario, ya sea completo o incompletoPatricia BullrichJorge Macri de JuntosPatricia Bullrich por Juntos por el CambioJorge Macri de JXC0.83.300.80.80.870.80.8710
13FemeninoMás de 50Nivel Secundario, ya sea completo o incompletoPatricia BullrichJorge Macri de JuntosPatricia Bullrich por Juntos por el CambioJorge Macri de JXC1.03.731.01.00.870.80.879
15FemeninoMás de 50Nivel Secundario, ya sea completo o incompletoSergio Massa y Agustín Rossi del Frente de Todos/Unión por la PatriaLeandro Santoro de Unión por la PatriaSergio Massa y Agustín Rossi del Frente de Todos/Unión por la PatriaLeandro Santoro de UPX1.03.831.01.01.050.91.057
41MasculinoMás de 50Nivel Secundario, ya sea completo o incompletoPatricia BullrichJorge Macri de JuntosPatricia Bullrich por Juntos por el CambioJorge Macri de JXC0.93.831.01.00.870.80.877
6Femenino30-49Nivel Universitario o Terciario, ya sea completo o incompletoPatricia BullrichJorge Macri de JuntosPatricia Bullrich por Juntos por el CambioJorge Macri de JXC0.93.600.90.90.871.00.876
35Masculino30-49Nivel Universitario o Terciario, ya sea completo o incompletoPatricia BullrichJorge Macri de JuntosPatricia Bullrich por Juntos por el CambioJorge Macri de JXC0.93.701.00.90.871.00.876
49MasculinoMás de 50Nivel Universitario o Terciario, ya sea completo o incompletoPatricia BullrichJorge Macri de JuntosPatricia Bullrich por Juntos por el CambioJorge Macri de JXC0.63.300.80.60.870.80.876
54MasculinoMás de 50Nivel Universitario o Terciario, ya sea completo o incompletoSergio Massa y Agustín Rossi del Frente de Todos/Unión por la PatriaLeandro Santoro de Unión por la PatriaSergio Massa y Agustín Rossi del Frente de Todos/Unión por la PatriaLeandro Santoro de UPX0.83.400.80.91.050.91.055